• How to perform a (LEFT|RIGHT|FULL) (INNER|OUTER) join with pandas?
• How do I add NaNs for missing rows after merge?
• How do I get rid of NaNs after merging?
• Can I merge on the index?
• Cross join with pandas?
• How do I merge multiple DataFrames?
• merge? join? concat? update? Who? What? Why?!

… and more. I’ve seen these recurring questions asking about various facets of the pandas merge functionality. Most of the information regarding merge and its various use cases today is fragmented across dozens of badly worded, unsearchable posts. The aim here is to collate some of the more important points for posterity.

This QnA is meant to be the next installment in a series of helpful user-guides on common pandas idioms (see this post on pivoting, and this post on concatenation, which I will be touching on, later).

Please note that this post is not meant to be a replacement for the documentation, so please read that as well! Some of the examples are taken from there.

This post aims to give readers a primer on SQL-flavoured merging with pandas, how to use it, and when not to use it.

In particular, here’s what this post will go through:

• The basics – types of joins (LEFT, RIGHT, OUTER, INNER)

  • merging with different column names
  • avoiding duplicate merge key column in output
• Merging with index under different conditions
  • effectively using your named index
  • merge key as the index of one and column of another
• Multiway merges on columns and indexes (unique and non-unique)
• Notable alternatives to merge and join

What this post will not go through:

• Performance-related discussions and timings (for now). Mostly notable mentions of better alternatives, wherever appropriate.
• Handling suffixes, removing extra columns, renaming outputs, and other specific use cases. There are other (read: better) posts that deal with that, so figure it out!

Note
Most examples default to INNER JOIN operations while demonstrating various features, unless otherwise specified.

Furthermore, all the DataFrames here can be copied and replicated so you can play with them. Also, see this post on how to read DataFrames from your clipboard.

Lastly, all visual representation of JOIN operations have been hand-drawn using Google Drawings. Inspiration from here.

Enough Talk, just show me how to use merge!

Setup

np.random.seed(0)
left = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': np.random.randn(4)})    
right = pd.DataFrame({'key': ['B', 'D', 'E', 'F'], 'value': np.random.randn(4)})

left

  key     value
0   A  1.764052
1   B  0.400157
2   C  0.978738
3   D  2.240893

right

  key     value
0   B  1.867558
1   D -0.977278
2   E  0.950088
3   F -0.151357

For the sake of simplicity, the key column has the same name (for now).

An INNER JOIN is represented by

Note
This, along with the forthcoming figures all follow this convention:

• blue indicates rows that are present in the merge result
• red indicates rows that are excluded from the result (i.e., removed)
• green indicates missing values that are replaced with NaNs in the result

To perform an INNER JOIN, call merge on the left DataFrame, specifying the right DataFrame and the join key (at the very least) as arguments.

left.merge(right, on='key')
# Or, if you want to be explicit
# left.merge(right, on='key', how='inner')

  key   value_x   value_y
0   B  0.400157  1.867558
1   D  2.240893 -0.977278

This returns only rows from left and right which share a common key (in this example, “B” and “D).

A LEFT OUTER JOIN, or LEFT JOIN is represented by

This can be performed by specifying how='left'.

left.merge(right, on='key', how='left')

  key   value_x   value_y
0   A  1.764052       NaN
1   B  0.400157  1.867558
2   C  0.978738       NaN
3   D  2.240893 -0.977278

Carefully note the placement of NaNs here. If you specify how='left', then only keys from left are used, and missing data from right is replaced by NaN.

And similarly, for a RIGHT OUTER JOIN, or RIGHT JOIN which is…

…specify how='right':

left.merge(right, on='key', how='right')

  key   value_x   value_y
0   B  0.400157  1.867558
1   D  2.240893 -0.977278
2   E       NaN  0.950088
3   F       NaN -0.151357

Here, keys from right are used, and missing data from left is replaced by NaN.

Finally, for the FULL OUTER JOIN, given by

specify how='outer'.

left.merge(right, on='key', how='outer')

  key   value_x   value_y
0   A  1.764052       NaN
1   B  0.400157  1.867558
2   C  0.978738       NaN
3   D  2.240893 -0.977278
4   E       NaN  0.950088
5   F       NaN -0.151357

This uses the keys from both frames, and NaNs are inserted for missing rows in both.

The documentation summarises these various merges nicely:

Other JOINs – LEFT-Excluding, RIGHT-Excluding, and FULL-Excluding/ANTI JOINs

If you need LEFT-Excluding JOINs and RIGHT-Excluding JOINs in two steps.

For LEFT-Excluding JOIN, represented as

Start by performing a LEFT OUTER JOIN and then filtering (excluding!) rows coming from left only,

(left.merge(right, on='key', how='left', indicator=True)
     .query('_merge == "left_only"')
     .drop('_merge', 1))

  key   value_x  value_y
0   A  1.764052      NaN
2   C  0.978738      NaN

Where,

left.merge(right, on='key', how='left', indicator=True)

  key   value_x   value_y     _merge
0   A  1.764052       NaN  left_only
1   B  0.400157  1.867558       both
2   C  0.978738       NaN  left_only
3   D  2.240893 -0.977278       both

And similarly, for a RIGHT-Excluding JOIN,

(left.merge(right, on='key', how='right', indicator=True)
     .query('_merge == "right_only"')
     .drop('_merge', 1))

  key  value_x   value_y
2   E      NaN  0.950088
3   F      NaN -0.151357

Lastly, if you are required to do a merge that only retains keys from the left or right, but not both (IOW, performing an ANTI-JOIN),

You can do this in similar fashion—

(left.merge(right, on='key', how='outer', indicator=True)
     .query('_merge != "both"')
     .drop('_merge', 1))

  key   value_x   value_y
0   A  1.764052       NaN
2   C  0.978738       NaN
4   E       NaN  0.950088
5   F       NaN -0.151357

Different names for key columns

If the key columns are named differently—for example, left has keyLeft, and right has keyRight instead of key—then you will have to specify left_on and right_on as arguments instead of on:

left2 = left.rename({'key':'keyLeft'}, axis=1)
right2 = right.rename({'key':'keyRight'}, axis=1)

left2

  keyLeft     value
0       A  1.764052
1       B  0.400157
2       C  0.978738
3       D  2.240893

right2

  keyRight     value
0        B  1.867558
1        D -0.977278
2        E  0.950088
3        F -0.151357

left2.merge(right2, left_on='keyLeft', right_on='keyRight', how='inner')

  keyLeft   value_x keyRight   value_y
0       B  0.400157        B  1.867558
1       D  2.240893        D -0.977278

Avoiding duplicate key column in output

When merging on keyLeft from left and keyRight from right, if you only want either of the keyLeft or keyRight (but not both) in the output, you can start by setting the index as a preliminary step.

left3 = left2.set_index('keyLeft')
left3.merge(right2, left_index=True, right_on='keyRight')

    value_x keyRight   value_y
0  0.400157        B  1.867558
1  2.240893        D -0.977278

Contrast this with the output of the command just before (thst is, the output of left2.merge(right2, left_on='keyLeft', right_on='keyRight', how='inner')), you’ll notice keyLeft is missing. You can figure out what column to keep based on which frame’s index is set as the key. This may matter when, say, performing some OUTER JOIN operation.

Merging only a single column from one of the DataFrames

For example, consider

right3 = right.assign(newcol=np.arange(len(right)))
right3
  key     value  newcol
0   B  1.867558       0
1   D -0.977278       1
2   E  0.950088       2
3   F -0.151357       3

If you are required to merge only “new_val” (without any of the other columns), you can usually just subset columns before merging:

left.merge(right3[['key', 'newcol']], on='key')

  key     value  newcol
0   B  0.400157       0
1   D  2.240893       1

If you’re doing a LEFT OUTER JOIN, a more performant solution would involve map:

# left['newcol'] = left['key'].map(right3.set_index('key')['newcol']))
left.assign(newcol=left['key'].map(right3.set_index('key')['newcol']))

  key     value  newcol
0   A  1.764052     NaN
1   B  0.400157     0.0
2   C  0.978738     NaN
3   D  2.240893     1.0

As mentioned, this is similar to, but faster than

left.merge(right3[['key', 'newcol']], on='key', how='left')

  key     value  newcol
0   A  1.764052     NaN
1   B  0.400157     0.0
2   C  0.978738     NaN
3   D  2.240893     1.0

Merging on multiple columns

To join on more than one column, specify a list for on (or left_on and right_on, as appropriate).

left.merge(right, on=['key1', 'key2'] ...)

Or, in the event the names are different,

left.merge(right, left_on=['lkey1', 'lkey2'], right_on=['rkey1', 'rkey2'])

Other useful merge* operations and functions

• Merging a DataFrame with Series on index: See this answer.

• Besides merge, DataFrame.update and DataFrame.combine_first are also used in certain cases to update one DataFrame with another.

• pd.merge_ordered is a useful function for ordered JOINs.

• pd.merge_asof (read: merge_asOf) is useful for approximate joins.

This section only covers the very basics, and is designed to only whet your appetite. For more examples and cases, see the documentation on merge, join, and concat as well as the links to the function specs.

Index-based *-JOIN (+ index-column merges)

Setup

np.random.seed([3, 14])
left = pd.DataFrame({'value': np.random.randn(4)}, index=['A', 'B', 'C', 'D'])    
right = pd.DataFrame({'value': np.random.randn(4)}, index=['B', 'D', 'E', 'F'])
left.index.name = right.index.name = 'idxkey'

left
           value
idxkey          
A      -0.602923
B      -0.402655
C       0.302329
D      -0.524349

right

           value
idxkey          
B       0.543843
D       0.013135
E      -0.326498
F       1.385076

Typically, a merge on index would look like this:

left.merge(right, left_index=True, right_index=True)


         value_x   value_y
idxkey                    
B      -0.402655  0.543843
D      -0.524349  0.013135

Support for index names

If your index is named, then v0.23 users can also specify the level name to on (or left_on and right_on as necessary).

left.merge(right, on='idxkey')

         value_x   value_y
idxkey                    
B      -0.402655  0.543843
D      -0.524349  0.013135

Merging on index of one, column(s) of another

It is possible (and quite simple) to use the index of one, and the column of another, to perform a merge. For example,

left.merge(right, left_on='key1', right_index=True)

Or vice versa (right_on=... and left_index=True).

right2 = right.reset_index().rename({'idxkey' : 'colkey'}, axis=1)
right2

  colkey     value
0      B  0.543843
1      D  0.013135
2      E -0.326498
3      F  1.385076

left.merge(right2, left_index=True, right_on='colkey')

    value_x colkey   value_y
0 -0.402655      B  0.543843
1 -0.524349      D  0.013135

In this special case, the index for left is named, so you can also use the index name with left_on, like this:

left.merge(right2, left_on='idxkey', right_on='colkey')

    value_x colkey   value_y
0 -0.402655      B  0.543843
1 -0.524349      D  0.013135

Besides these, there is another succinct option. You can use DataFrame.join which defaults to joins on the index. DataFrame.join does a LEFT OUTER JOIN by default, so how='inner' is necessary here.

left.join(right, how='inner', lsuffix='_x', rsuffix='_y')

         value_x   value_y
idxkey                    
B      -0.402655  0.543843
D      -0.524349  0.013135

Note that I needed to specify the lsuffix and rsuffix arguments since join would otherwise error out:

left.join(right)
ValueError: columns overlap but no suffix specified: Index(['value'], dtype='object')

Since the column names are the same. This would not be a problem if they were differently named.

left.rename(columns={'value':'leftvalue'}).join(right, how='inner')

        leftvalue     value
idxkey                     
B       -0.402655  0.543843
D       -0.524349  0.013135

Lastly, as an alternative for index-based joins, you can use pd.concat:

pd.concat([left, right], axis=1, sort=False, join='inner')

           value     value
idxkey                    
B      -0.402655  0.543843
D      -0.524349  0.013135

Omit join='inner' if you need a FULL OUTER JOIN (the default):

pd.concat([left, right], axis=1, sort=False)

      value     value
A -0.602923       NaN
B -0.402655  0.543843
C  0.302329       NaN
D -0.524349  0.013135
E       NaN -0.326498
F       NaN  1.385076

For more information, see this canonical post on pd.concat by @piRSquared.

Generalizing: mergeing multiple DataFrames

Oftentimes, the situation arises when multiple DataFrames are to be merged together. Naively, this can be done by chaining merge calls:

df1.merge(df2, ...).merge(df3, ...)

However, this quickly gets out of hand for many DataFrames. Furthermore, it may be necessary to generalise for an unknown number of DataFrames.

Here I introduce pd.concat for multi-way joins on unique keys, and DataFrame.join for multi-way joins on non-unique keys. First, the setup.

# Setup.
np.random.seed(0)
A = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'valueA': np.random.randn(4)})    
B = pd.DataFrame({'key': ['B', 'D', 'E', 'F'], 'valueB': np.random.randn(4)})
C = pd.DataFrame({'key': ['D', 'E', 'J', 'C'], 'valueC': np.ones(4)})
dfs = [A, B, C] 

# Note, the "key" column values are unique, so the index is unique.
A2 = A.set_index('key')
B2 = B.set_index('key')
C2 = C.set_index('key')

dfs2 = [A2, B2, C2]

Multiway merge on unique keys (or index)

If your keys (here, the key could either be a column or an index) are unique, then you can use pd.concat. Note that pd.concat joins DataFrames on the index.

# merge on `key` column, you'll need to set the index before concatenating
pd.concat([
    df.set_index('key') for df in dfs], axis=1, join='inner'
).reset_index()

  key    valueA    valueB  valueC
0   D  2.240893 -0.977278     1.0

# merge on `key` index
pd.concat(dfs2, axis=1, sort=False, join='inner')

       valueA    valueB  valueC
key                            
D    2.240893 -0.977278     1.0

Omit join='inner' for a FULL OUTER JOIN. Note that you cannot specify LEFT or RIGHT OUTER joins (if you need these, use join, described below).

Multiway merge on keys with duplicates

concat is fast, but has its shortcomings. It cannot handle duplicates.

A3 = pd.DataFrame({'key': ['A', 'B', 'C', 'D', 'D'], 'valueA': np.random.randn(5)})

pd.concat([df.set_index('key') for df in [A3, B, C]], axis=1, join='inner')
ValueError: Shape of passed values is (3, 4), indices imply (3, 2)

In this situation, we can use join since it can handle non-unique keys (note that join joins DataFrames on their index; it calls merge under the hood and does a LEFT OUTER JOIN unless otherwise specified).

# join on `key` column, set as the index first
# For inner join. For left join, omit the "how" argument.
A.set_index('key').join(
    [df.set_index('key') for df in (B, C)], how='inner').reset_index()

  key    valueA    valueB  valueC
0   D  2.240893 -0.977278     1.0

# join on `key` index
A3.set_index('key').join([B2, C2], how='inner')

       valueA    valueB  valueC
key                            
D    1.454274 -0.977278     1.0
D    0.761038 -0.977278     1.0

A supplemental visual view of pd.concat([df0, df1], kwargs). Notice that, kwarg axis=0 or axis=1 ‘s meaning is not as intuitive as df.mean() or df.apply(func)